Fuzzy Neural Networks for Decision Support in Negotiation
- University of Peloponnese, 22100 Tripoli (Libyan Arab Jamahiriya)
There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy sets which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.
- OSTI ID:
- 21251782
- Journal Information:
- AIP Conference Proceedings, Vol. 1060, Issue 1; Other Information: DOI: 10.1063/1.3037115; (c) 2008 American Institute of Physics; IeCCS 2007: International electronic conference on computer science, 28 June - 8 July 2007; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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